Neural Networks for Tabular Data and Car Price Prediction

Predicting market values accurately is a cornerstone of modern data-driven industries. This text-based course guides you through applying neural networks to structured tabular data, using the practical challenge of car price prediction as your core learning scenario. You will transition from understanding basic data concepts to designing, training, and evaluating a neural network regression model. Through clear written explanations and structured code walk-throughs, you will learn how to handle real-world data challenges, prepare features properly, and optimize deep learning models for numerical forecasting. What you'll learn: - Understand the foundational concepts of neural networks and regression tasks - Prepare and preprocess structured tabular datasets using modern data manipulation techniques - Engineer features by handling categorical variables, missing values, and numerical scaling - Build and configure a regression neural network using modern deep learning frameworks - Evaluate model performance using key regression metrics like Mean Squared Error and R-squared - Apply best practices for preventing overfitting, including validation splits and regularization The course begins with essential terminology and data preparation fundamentals before guiding you step-by-step through network architecture design, model training, and performance tuning. This program is designed for beginners in machine learning and data analysis, requiring only a basic understanding of Python to get started. Start reading today to unlock the power of neural networks for practical forecasting.